Conference paper

OČENÁŠEK Jiří and SCHWARZ Josef. The Parallel Bayesian Optimization Algorithm. In: Proceedings of the European Symposium on Computational Inteligence. Košice: Springer Verlag, 2000, pp. 61-67. ISBN 3-7908-1322-2. ISSN 1615-3871.
Publication language:english
Original title:The Parallel Bayesian Optimization Algorithm
Proceedings:Proceedings of the European Symposium on Computational Inteligence
Conference:ISCI 2000
Place:Košice, SK
Publisher:Springer Verlag
EDA, BOA, Bayesian network, probabilistic model, fine-grained parallelism, parallel computing
In the last few years there has been a growing interest in the field of Estimation of Distribution Algorithms (EDAs), where crossover and mutation genetic operators are replaced by probability estimation and sampling techniques. The Bayesian Optimization Algorithm incorporates methods for learning Bayesian networks and uses these to model the promising solutions and generate new ones. The aim of this paper is to propose the parallel version of this algorithm, where the optimization time decreases linearly with the number of processors. During the parallel construction of network, the explicit topological ordering of variables is used to keep the model acyclic. The performance of the optimization process seems to be not affected by this constraint and our version of algorithm was successfully tested for the discrete combinatorial problem represented by graph partitioning as well as for deceptive functions.
   author = {Ji{\v{r}}{\'{i}} O{\v{c}}en{\'{a}}{\v{s}}ek and Josef
   title = {The Parallel Bayesian Optimization Algorithm},
   pages = {61--67},
   booktitle = {Proceedings of the European Symposium on Computational
   year = {2000},
   location = {Ko{\v{s}}ice, SK},
   publisher = {Springer Verlag},
   ISBN = {3-7908-1322-2},
   ISSN = {1615-3871},
   language = {english},
   url = {}

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